MétaCan
Menu
Back to cohort
Record W4393790346 · doi:10.5281/zenodo.4746506

OH (3-1) nightglow volume emission rate and its characteristics based on OSIRIS measurements

2021· dataset· en· W4393790346 on OpenAlexaff
Anqi Li, Christopher Z. Roth, Kristell Pérot, Ole Martin Christensen, Adam Bourassa, Douglas A. Degenstein, D. Murtagh

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsOsirisAirglowVolume (thermodynamics)Environmental sciencePhysicsAtmospheric sciencesThermodynamicsEcologyBiology

Abstract

fetched live from OpenAlex

The nightglow emission originating from the vibrationally excited hydroxyl layer (about 85 km altitude) has been measured by the infrared imager (IRI) on the Odin satellite for more than 15 years. The calibrated limb radiance is used to derive the volume emission rate (VER) profile using the optimal estimation method. The resulting VER profiles are from 60 to 95 km with a 1 km resolution. Each profile is used to estimate the nightglow layer peak intensity, peak height and thickness by fitting a Gaussian function using the nonlinear weighted least-square method. Detailed retrieval steps are described in Li et al. 2021. Data sets are structured in one year per file in NetCDF format. File names are in the format of `iri_ch1_ver_(year).nc', such as `iri_ch1_ver_2008.nc' for data collected in 2008. The individual parameters included are described in the README.pdf file.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.016

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.229
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicAtmospheric Ozone and ClimateFrench-language works237,207